{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incidence du syndrome grippal" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek\n", "import os\n", "from urllib.request import urlretrieve" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour nous protéger contre une éventuelle disparition ou modification du serveur du Réseau Sentinelles, nous faisons une copie locale de ce jeux de données que nous préservons avec notre analyse. Il est inutile et même risquée de télécharger les données à chaque exécution, car dans le cas d'une panne nous pourrions remplacer nos données par un fichier défectueux. Pour cette raison, nous téléchargeons les données seulement si la copie locale n'existe pas." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "data_file = 'syndrome-grippal.csv' \n", "if not os.path.exists(data_file):\n", " urlretrieve(data_url, data_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n", "\n", "| Nom de colonne | Libellé de colonne |\n", "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n", "| week | Semaine calendaire (ISO 8601) |\n", "| indicator | Code de l'indicateur de surveillance |\n", "| inc | Estimation de l'incidence de consultations en nombre de cas |\n", "| inc_low | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation |\n", "| inc_up | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation |\n", "| inc100 | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", "| inc100_low | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", "| inc100_up | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n", "| geo_insee | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/ |\n", "| geo_name | Libellé de la zone géographique (ce libellé peut être modifié sans préavis) |\n", "\n", "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020253332098214708.027256.03122.040.0FRFrance
120253232003914417.025661.03022.038.0FRFrance
220253131847012625.024315.02819.037.0FRFrance
320253031916614283.024049.02922.036.0FRFrance
420252931867313815.023531.02821.035.0FRFrance
520252832328518131.028439.03527.043.0FRFrance
620252732145317129.025777.03226.038.0FRFrance
720252632194517422.026468.03326.040.0FRFrance
820252532332318546.028100.03528.042.0FRFrance
920252432315418577.027731.03528.042.0FRFrance
1020252332439119307.029475.03628.044.0FRFrance
1120252231875514333.023177.02821.035.0FRFrance
1220252132376018671.028849.03527.043.0FRFrance
1320252032026515814.024716.03023.037.0FRFrance
1420251931626412394.020134.02418.030.0FRFrance
1520251831811513975.022255.02721.033.0FRFrance
1620251732215017291.027009.03326.040.0FRFrance
1720251632856422550.034578.04334.052.0FRFrance
1820251533572129592.041850.05344.062.0FRFrance
1920251433757931232.043926.05647.065.0FRFrance
2020251333967333686.045660.05950.068.0FRFrance
2120251235254345627.059459.07868.088.0FRFrance
2220251135946952154.066784.08978.0100.0FRFrance
2320251036033453048.067620.09079.0101.0FRFrance
2420250938453174994.094068.0126112.0140.0FRFrance
252025083136020124824.0147216.0203186.0220.0FRFrance
262025073208952195988.0221916.0312293.0331.0FRFrance
272025063273519258159.0288879.0408385.0431.0FRFrance
282025053334395318416.0350374.0499475.0523.0FRFrance
292025043350043332885.0367201.0522496.0548.0FRFrance
.................................
209919852132609619621.032571.04735.059.0FRFrance
210019852032789620885.034907.05138.064.0FRFrance
210119851934315432821.053487.07859.097.0FRFrance
210219851834055529935.051175.07455.093.0FRFrance
210319851733405324366.043740.06244.080.0FRFrance
210419851635036236451.064273.09166.0116.0FRFrance
210519851536388145538.082224.011683.0149.0FRFrance
21061985143134545114400.0154690.0244207.0281.0FRFrance
21071985133197206176080.0218332.0357319.0395.0FRFrance
21081985123245240223304.0267176.0445405.0485.0FRFrance
21091985113276205252399.0300011.0501458.0544.0FRFrance
21101985103353231326279.0380183.0640591.0689.0FRFrance
21111985093369895341109.0398681.0670618.0722.0FRFrance
21121985083389886359529.0420243.0707652.0762.0FRFrance
21131985073471852432599.0511105.0855784.0926.0FRFrance
21141985063565825518011.0613639.01026939.01113.0FRFrance
21151985053637302592795.0681809.011551074.01236.0FRFrance
21161985043424937390794.0459080.0770708.0832.0FRFrance
21171985033213901174689.0253113.0388317.0459.0FRFrance
211819850239758680949.0114223.0177147.0207.0FRFrance
211919850138548965918.0105060.0155120.0190.0FRFrance
212019845238483060602.0109058.0154110.0198.0FRFrance
2121198451310172680242.0123210.0185146.0224.0FRFrance
21221984503123680101401.0145959.0225184.0266.0FRFrance
2123198449310107381684.0120462.0184149.0219.0FRFrance
212419844837862060634.096606.0143110.0176.0FRFrance
212519844737202954274.089784.013199.0163.0FRFrance
212619844638733067686.0106974.0159123.0195.0FRFrance
21271984453135223101414.0169032.0246184.0308.0FRFrance
212819844436842220056.0116788.012537.0213.0FRFrance
\n", "

2129 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202533 3 20982 14708.0 27256.0 31 22.0 \n", "1 202532 3 20039 14417.0 25661.0 30 22.0 \n", "2 202531 3 18470 12625.0 24315.0 28 19.0 \n", "3 202530 3 19166 14283.0 24049.0 29 22.0 \n", "4 202529 3 18673 13815.0 23531.0 28 21.0 \n", "5 202528 3 23285 18131.0 28439.0 35 27.0 \n", "6 202527 3 21453 17129.0 25777.0 32 26.0 \n", "7 202526 3 21945 17422.0 26468.0 33 26.0 \n", "8 202525 3 23323 18546.0 28100.0 35 28.0 \n", "9 202524 3 23154 18577.0 27731.0 35 28.0 \n", "10 202523 3 24391 19307.0 29475.0 36 28.0 \n", "11 202522 3 18755 14333.0 23177.0 28 21.0 \n", "12 202521 3 23760 18671.0 28849.0 35 27.0 \n", "13 202520 3 20265 15814.0 24716.0 30 23.0 \n", "14 202519 3 16264 12394.0 20134.0 24 18.0 \n", "15 202518 3 18115 13975.0 22255.0 27 21.0 \n", "16 202517 3 22150 17291.0 27009.0 33 26.0 \n", "17 202516 3 28564 22550.0 34578.0 43 34.0 \n", "18 202515 3 35721 29592.0 41850.0 53 44.0 \n", "19 202514 3 37579 31232.0 43926.0 56 47.0 \n", "20 202513 3 39673 33686.0 45660.0 59 50.0 \n", "21 202512 3 52543 45627.0 59459.0 78 68.0 \n", "22 202511 3 59469 52154.0 66784.0 89 78.0 \n", "23 202510 3 60334 53048.0 67620.0 90 79.0 \n", "24 202509 3 84531 74994.0 94068.0 126 112.0 \n", "25 202508 3 136020 124824.0 147216.0 203 186.0 \n", "26 202507 3 208952 195988.0 221916.0 312 293.0 \n", "27 202506 3 273519 258159.0 288879.0 408 385.0 \n", "28 202505 3 334395 318416.0 350374.0 499 475.0 \n", "29 202504 3 350043 332885.0 367201.0 522 496.0 \n", "... ... ... ... ... ... ... ... \n", "2099 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2100 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2101 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2102 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2103 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2104 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2105 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2106 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2107 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2108 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2109 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2110 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2111 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2112 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2113 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2114 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2115 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2116 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2117 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2118 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2119 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2120 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2121 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2122 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2123 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2124 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2125 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2126 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2127 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2128 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 40.0 FR France \n", "1 38.0 FR France \n", "2 37.0 FR France \n", "3 36.0 FR France \n", "4 35.0 FR France \n", "5 43.0 FR France \n", "6 38.0 FR France \n", "7 40.0 FR France \n", "8 42.0 FR France \n", "9 42.0 FR France \n", "10 44.0 FR France \n", "11 35.0 FR France \n", "12 43.0 FR France \n", "13 37.0 FR France \n", "14 30.0 FR France \n", "15 33.0 FR France \n", "16 40.0 FR France \n", "17 52.0 FR France \n", "18 62.0 FR France \n", "19 65.0 FR France \n", "20 68.0 FR France \n", "21 88.0 FR France \n", "22 100.0 FR France \n", "23 101.0 FR France \n", "24 140.0 FR France \n", "25 220.0 FR France \n", "26 331.0 FR France \n", "27 431.0 FR France \n", "28 523.0 FR France \n", "29 548.0 FR France \n", "... ... ... ... \n", "2099 59.0 FR France \n", "2100 64.0 FR France \n", "2101 97.0 FR France \n", "2102 93.0 FR France \n", "2103 80.0 FR France \n", "2104 116.0 FR France \n", "2105 149.0 FR France \n", "2106 281.0 FR France \n", "2107 395.0 FR France \n", "2108 485.0 FR France \n", "2109 544.0 FR France \n", "2110 689.0 FR France \n", "2111 722.0 FR France \n", "2112 762.0 FR France \n", "2113 926.0 FR France \n", "2114 1113.0 FR France \n", "2115 1236.0 FR France \n", "2116 832.0 FR France \n", "2117 459.0 FR France \n", "2118 207.0 FR France \n", "2119 190.0 FR France \n", "2120 198.0 FR France \n", "2121 224.0 FR France \n", "2122 266.0 FR France \n", "2123 219.0 FR France \n", "2124 176.0 FR France \n", "2125 163.0 FR France \n", "2126 195.0 FR France \n", "2127 308.0 FR France \n", "2128 213.0 FR France \n", "\n", "[2129 rows x 10 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_file, skiprows=1)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y a-t-il des points manquants dans ce jeux de données ? Oui, la semaine 19 de l'année 1989 n'a pas de valeurs associées." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
18921989193-NaNNaN-NaNNaNFRFrance
\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", "1892 198919 3 - NaN NaN - NaN NaN \n", "\n", " geo_insee geo_name \n", "1892 FR France " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020253332098214708.027256.03122.040.0FRFrance
120253232003914417.025661.03022.038.0FRFrance
220253131847012625.024315.02819.037.0FRFrance
320253031916614283.024049.02922.036.0FRFrance
420252931867313815.023531.02821.035.0FRFrance
520252832328518131.028439.03527.043.0FRFrance
620252732145317129.025777.03226.038.0FRFrance
720252632194517422.026468.03326.040.0FRFrance
820252532332318546.028100.03528.042.0FRFrance
920252432315418577.027731.03528.042.0FRFrance
1020252332439119307.029475.03628.044.0FRFrance
1120252231875514333.023177.02821.035.0FRFrance
1220252132376018671.028849.03527.043.0FRFrance
1320252032026515814.024716.03023.037.0FRFrance
1420251931626412394.020134.02418.030.0FRFrance
1520251831811513975.022255.02721.033.0FRFrance
1620251732215017291.027009.03326.040.0FRFrance
1720251632856422550.034578.04334.052.0FRFrance
1820251533572129592.041850.05344.062.0FRFrance
1920251433757931232.043926.05647.065.0FRFrance
2020251333967333686.045660.05950.068.0FRFrance
2120251235254345627.059459.07868.088.0FRFrance
2220251135946952154.066784.08978.0100.0FRFrance
2320251036033453048.067620.09079.0101.0FRFrance
2420250938453174994.094068.0126112.0140.0FRFrance
252025083136020124824.0147216.0203186.0220.0FRFrance
262025073208952195988.0221916.0312293.0331.0FRFrance
272025063273519258159.0288879.0408385.0431.0FRFrance
282025053334395318416.0350374.0499475.0523.0FRFrance
292025043350043332885.0367201.0522496.0548.0FRFrance
.................................
209919852132609619621.032571.04735.059.0FRFrance
210019852032789620885.034907.05138.064.0FRFrance
210119851934315432821.053487.07859.097.0FRFrance
210219851834055529935.051175.07455.093.0FRFrance
210319851733405324366.043740.06244.080.0FRFrance
210419851635036236451.064273.09166.0116.0FRFrance
210519851536388145538.082224.011683.0149.0FRFrance
21061985143134545114400.0154690.0244207.0281.0FRFrance
21071985133197206176080.0218332.0357319.0395.0FRFrance
21081985123245240223304.0267176.0445405.0485.0FRFrance
21091985113276205252399.0300011.0501458.0544.0FRFrance
21101985103353231326279.0380183.0640591.0689.0FRFrance
21111985093369895341109.0398681.0670618.0722.0FRFrance
21121985083389886359529.0420243.0707652.0762.0FRFrance
21131985073471852432599.0511105.0855784.0926.0FRFrance
21141985063565825518011.0613639.01026939.01113.0FRFrance
21151985053637302592795.0681809.011551074.01236.0FRFrance
21161985043424937390794.0459080.0770708.0832.0FRFrance
21171985033213901174689.0253113.0388317.0459.0FRFrance
211819850239758680949.0114223.0177147.0207.0FRFrance
211919850138548965918.0105060.0155120.0190.0FRFrance
212019845238483060602.0109058.0154110.0198.0FRFrance
2121198451310172680242.0123210.0185146.0224.0FRFrance
21221984503123680101401.0145959.0225184.0266.0FRFrance
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212419844837862060634.096606.0143110.0176.0FRFrance
212519844737202954274.089784.013199.0163.0FRFrance
212619844638733067686.0106974.0159123.0195.0FRFrance
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212819844436842220056.0116788.012537.0213.0FRFrance
\n", "

2128 rows × 10 columns

\n", "
" ], "text/plain": [ " week indicator inc inc_low inc_up inc100 inc100_low \\\n", "0 202533 3 20982 14708.0 27256.0 31 22.0 \n", "1 202532 3 20039 14417.0 25661.0 30 22.0 \n", "2 202531 3 18470 12625.0 24315.0 28 19.0 \n", "3 202530 3 19166 14283.0 24049.0 29 22.0 \n", "4 202529 3 18673 13815.0 23531.0 28 21.0 \n", "5 202528 3 23285 18131.0 28439.0 35 27.0 \n", "6 202527 3 21453 17129.0 25777.0 32 26.0 \n", "7 202526 3 21945 17422.0 26468.0 33 26.0 \n", "8 202525 3 23323 18546.0 28100.0 35 28.0 \n", "9 202524 3 23154 18577.0 27731.0 35 28.0 \n", "10 202523 3 24391 19307.0 29475.0 36 28.0 \n", "11 202522 3 18755 14333.0 23177.0 28 21.0 \n", "12 202521 3 23760 18671.0 28849.0 35 27.0 \n", "13 202520 3 20265 15814.0 24716.0 30 23.0 \n", "14 202519 3 16264 12394.0 20134.0 24 18.0 \n", "15 202518 3 18115 13975.0 22255.0 27 21.0 \n", "16 202517 3 22150 17291.0 27009.0 33 26.0 \n", "17 202516 3 28564 22550.0 34578.0 43 34.0 \n", "18 202515 3 35721 29592.0 41850.0 53 44.0 \n", "19 202514 3 37579 31232.0 43926.0 56 47.0 \n", "20 202513 3 39673 33686.0 45660.0 59 50.0 \n", "21 202512 3 52543 45627.0 59459.0 78 68.0 \n", "22 202511 3 59469 52154.0 66784.0 89 78.0 \n", "23 202510 3 60334 53048.0 67620.0 90 79.0 \n", "24 202509 3 84531 74994.0 94068.0 126 112.0 \n", "25 202508 3 136020 124824.0 147216.0 203 186.0 \n", "26 202507 3 208952 195988.0 221916.0 312 293.0 \n", "27 202506 3 273519 258159.0 288879.0 408 385.0 \n", "28 202505 3 334395 318416.0 350374.0 499 475.0 \n", "29 202504 3 350043 332885.0 367201.0 522 496.0 \n", "... ... ... ... ... ... ... ... \n", "2099 198521 3 26096 19621.0 32571.0 47 35.0 \n", "2100 198520 3 27896 20885.0 34907.0 51 38.0 \n", "2101 198519 3 43154 32821.0 53487.0 78 59.0 \n", "2102 198518 3 40555 29935.0 51175.0 74 55.0 \n", "2103 198517 3 34053 24366.0 43740.0 62 44.0 \n", "2104 198516 3 50362 36451.0 64273.0 91 66.0 \n", "2105 198515 3 63881 45538.0 82224.0 116 83.0 \n", "2106 198514 3 134545 114400.0 154690.0 244 207.0 \n", "2107 198513 3 197206 176080.0 218332.0 357 319.0 \n", "2108 198512 3 245240 223304.0 267176.0 445 405.0 \n", "2109 198511 3 276205 252399.0 300011.0 501 458.0 \n", "2110 198510 3 353231 326279.0 380183.0 640 591.0 \n", "2111 198509 3 369895 341109.0 398681.0 670 618.0 \n", "2112 198508 3 389886 359529.0 420243.0 707 652.0 \n", "2113 198507 3 471852 432599.0 511105.0 855 784.0 \n", "2114 198506 3 565825 518011.0 613639.0 1026 939.0 \n", "2115 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", "2116 198504 3 424937 390794.0 459080.0 770 708.0 \n", "2117 198503 3 213901 174689.0 253113.0 388 317.0 \n", "2118 198502 3 97586 80949.0 114223.0 177 147.0 \n", "2119 198501 3 85489 65918.0 105060.0 155 120.0 \n", "2120 198452 3 84830 60602.0 109058.0 154 110.0 \n", "2121 198451 3 101726 80242.0 123210.0 185 146.0 \n", "2122 198450 3 123680 101401.0 145959.0 225 184.0 \n", "2123 198449 3 101073 81684.0 120462.0 184 149.0 \n", "2124 198448 3 78620 60634.0 96606.0 143 110.0 \n", "2125 198447 3 72029 54274.0 89784.0 131 99.0 \n", "2126 198446 3 87330 67686.0 106974.0 159 123.0 \n", "2127 198445 3 135223 101414.0 169032.0 246 184.0 \n", "2128 198444 3 68422 20056.0 116788.0 125 37.0 \n", "\n", " inc100_up geo_insee geo_name \n", "0 40.0 FR France \n", "1 38.0 FR France \n", "2 37.0 FR France \n", "3 36.0 FR France \n", "4 35.0 FR France \n", "5 43.0 FR France \n", "6 38.0 FR France \n", "7 40.0 FR France \n", "8 42.0 FR France \n", "9 42.0 FR France \n", "10 44.0 FR France \n", "11 35.0 FR France \n", "12 43.0 FR France \n", "13 37.0 FR France \n", "14 30.0 FR France \n", "15 33.0 FR France \n", "16 40.0 FR France \n", "17 52.0 FR France \n", "18 62.0 FR France \n", "19 65.0 FR France \n", "20 68.0 FR France \n", "21 88.0 FR France \n", "22 100.0 FR France \n", "23 101.0 FR France \n", "24 140.0 FR France \n", "25 220.0 FR France \n", "26 331.0 FR France \n", "27 431.0 FR France \n", "28 523.0 FR France \n", "29 548.0 FR France \n", "... ... ... ... \n", "2099 59.0 FR France \n", "2100 64.0 FR France \n", "2101 97.0 FR France \n", "2102 93.0 FR France \n", "2103 80.0 FR France \n", "2104 116.0 FR France \n", "2105 149.0 FR France \n", "2106 281.0 FR France \n", "2107 395.0 FR France \n", "2108 485.0 FR France \n", "2109 544.0 FR France \n", "2110 689.0 FR France \n", "2111 722.0 FR France \n", "2112 762.0 FR France \n", "2113 926.0 FR France \n", "2114 1113.0 FR France \n", "2115 1236.0 FR France \n", "2116 832.0 FR France \n", "2117 459.0 FR France \n", "2118 207.0 FR France \n", "2119 190.0 FR France \n", "2120 198.0 FR France \n", "2121 224.0 FR France \n", "2122 266.0 FR France \n", "2123 219.0 FR France \n", "2124 176.0 FR France \n", "2125 163.0 FR France \n", "2126 195.0 FR France \n", "2127 308.0 FR France \n", "2128 213.0 FR France \n", "\n", "[2128 rows x 10 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.dropna().copy()\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nos données utilisent une convention inhabituelle: le numéro de\n", "semaine est collé à l'année, donnant l'impression qu'il s'agit\n", "de nombre entier. C'est comme ça que Pandas les interprète.\n", " \n", "Un deuxième problème est que Pandas ne comprend pas les numéros de\n", "semaine. Il faut lui fournir les dates de début et de fin de\n", "semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n", "\n", "Comme la conversion des semaines est devenu assez complexe, nous\n", "écrivons une petite fonction Python pour cela. Ensuite, nous\n", "l'appliquons à tous les points de nos donnés. Les résultats vont\n", "dans une nouvelle colonne 'period'." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def convert_week(year_and_week_int):\n", " year_and_week_str = str(year_and_week_int)\n", " year = int(year_and_week_str[:4])\n", " week = int(year_and_week_str[4:])\n", " w = isoweek.Week(year, week)\n", " return pd.Period(w.day(0), 'W')\n", "\n", "data['period'] = [convert_week(yw) for yw in data['week']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il restent deux petites modifications à faire.\n", "\n", "Premièrement, nous définissons les périodes d'observation\n", "comme nouvel index de notre jeux de données. Ceci en fait\n", "une suite chronologique, ce qui sera pratique par la suite.\n", "\n", "Deuxièmement, nous trions les points par période, dans\n", "le sens chronologique." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous vérifions la cohérence des données. Entre la fin d'une période et\n", "le début de la période qui suit, la différence temporelle doit être\n", "zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\"\n", "d'une seconde.\n", "\n", "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives\n", "entre lesquelles il manque une semaine.\n", "\n", "Nous reconnaissons ces dates: c'est la semaine sans observations\n", "que nous avions supprimées !" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n" ] } ], "source": [ "periods = sorted_data.index\n", "for p1, p2 in zip(periods[:-1], periods[1:]):\n", " delta = p2.to_timestamp() - p1.end_time\n", " if delta > pd.Timedelta('1s'):\n", " print(p1, p2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un premier regard sur les données !" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": true }, "outputs": [ { "ename": "TypeError", "evalue": "Empty 'DataFrame': no numeric data to plot", "output_type": "error", "traceback": [ 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 251\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 364\u001b[0m raise TypeError('Empty {0!r}: no numeric data to '\n\u001b[0;32m--> 365\u001b[0;31m 'plot'.format(numeric_data.__class__.__name__))\n\u001b[0m\u001b[1;32m 366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: Empty 'DataFrame': no numeric data to plot" ] } ], "source": [ "sorted_data['inc'].plot()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "sorted_data['inc']=sorted_data['inc'].astype(int)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en été." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['inc'][-200:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Etude de l'incidence annuelle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Etant donné que le pic de l'épidémie se situe en hiver, à cheval\n", "entre deux années civiles, nous définissons la période de référence\n", "entre deux minima de l'incidence, du 1er août de l'année $N$ au\n", "1er août de l'année $N+1$.\n", "\n", "Notre tâche est un peu compliquée par le fait que l'année ne comporte\n", "pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n", "de référence: à la place du 1er août de chaque année, nous utilisons le\n", "premier jour de la semaine qui contient le 1er août.\n", "\n", "Comme l'incidence de syndrome grippal est très faible en été, cette\n", "modification ne risque pas de fausser nos conclusions.\n", "\n", "Encore un petit détail: les données commencent an octobre 1984, ce qui\n", "rend la première année incomplète. Nous commençons donc l'analyse en 1985." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", " for y in range(1985,\n", " sorted_data.index[-1].year)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n", "\n", "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "year = []\n", "yearly_incidence = []\n", "for week1, week2 in zip(first_august_week[:-1],\n", " first_august_week[1:]):\n", " one_year = sorted_data['inc'][week1:week2-1]\n", " assert abs(len(one_year)-52) < 2\n", " yearly_incidence.append(one_year.sum())\n", " year.append(week2.year)\n", "yearly_incidence = pd.Series(data=yearly_incidence, index=year)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voici les incidences annuelles." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yearly_incidence.plot(style='*')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2021 743449\n", "2014 1600941\n", "1991 1659249\n", "1995 1840410\n", "2020 2010315\n", "2022 2060304\n", "2012 2175217\n", "2003 2234584\n", "2019 2254386\n", "2006 2307352\n", "2017 2321583\n", "2001 2529279\n", "1992 2574578\n", "1993 2703886\n", "2018 2705325\n", "1988 2765617\n", "2007 2780164\n", "1987 2855570\n", "2016 2856393\n", "2011 2857040\n", "2023 2873501\n", "2008 2973918\n", "1998 3034904\n", "2002 3125418\n", "2009 3444020\n", "1994 3514763\n", "1996 3539413\n", "2004 3567744\n", "1997 3620066\n", "2015 3654892\n", "2024 3670417\n", "2000 3826372\n", "2005 3835025\n", "1999 3908112\n", "2010 4111392\n", "2013 4182691\n", "1986 5115251\n", "1990 5235827\n", "1989 5466192\n", "dtype: int64" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_incidence.sort_values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n", " française, sont assez rares: il y en eu trois au cours des 35 dernières années." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yearly_incidence.hist(xrot=20)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 1 }